Constructing Novel Prognostic Biomarkers of Advanced Nasopharyngeal Carcinoma from Multiparametric MRI Radiomics Using Ensemble-Model Based Iterative Feature Selection

Ting Ting Yu, Sai Kit Lam, Lok Hang To, Ka Yan Tse, Nong Yi Cheng, Yeuk Nam Fan, Cheuk Lai Lo, Ka Wa Or, Man Lok Chan, Ka Ching Hui, Fong Chi Chan, Wai Ming Hui, Lo Kin Ngai, Francis Kar Ho Lee, Kwok Hung Au, Celia Wai Yi Yip, Yong Zhang, Jing Cai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Although different treatment strategies have been developed for nasopharyngeal carcinoma (NPC), recurrence and distant metastasis remain major challenges to advanced NPC. This study aims to identify pre-treatment radiomics models to predict progression-free survival (PFS) using pre-treatment T2weighted short tau inversion recovery (STIR) magnetic resonance (MR) images and contrast-enhanced T1-weighted MR images (CET1-W) separately. To address the problem of imbalanced and small dataset in model training, we developed a novel method named as ensemble-model based iterative feature selection for determine the predictive feature sets. Least absolute shrinkage and selection operator (LASSO) was used in both feature selection and model construction. Model ensemble was constructed from the subset of patients during the process of feature selection and model construction. In model construction, selected models built from predictive feature sets were then internally validated using 1000-bootstrapping for whole-patient cohort. Corrected AUC of Joint CET1-w and T2-w model was the highest and corrected AUC of T2-w modes was the lowest. Rad-scores were calculated as a linear combination of selected features for each patient, and were evaluated by stratified Kaplan-Meier analysis and Cox proportional hazard regression. Significant differences (\mathrm{p}\lt 0.001) were observed between survival curves of high-risk and low-risk patients stratified by Rad-scores. Our results demonstrated the capability of the ensemble-model based iterative feature selection method for imbalanced and small dataset when building MRI-based biomarkers to stratify patients into high risk and low risk.

Original languageEnglish
Title of host publication2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148557
DOIs
Publication statusPublished - Nov 2019
Event2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019 - Shenzhen, China
Duration: 22 Nov 201924 Nov 2019

Publication series

Name2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019

Conference

Conference2019 International Conference on Medical Imaging Physics and Engineering, ICMIPE 2019
Country/TerritoryChina
CityShenzhen
Period22/11/1924/11/19

Keywords

  • cox proportional hazard regression
  • nasopharyngeal carcinoma
  • progression-free survival
  • radiomics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Media Technology
  • Radiology Nuclear Medicine and imaging

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